User Guide¶

Array Conversion¶

The function bob.core.convert() allows you to convert objects of type numpy.ndarray or bob.blitz.array between different types, with range compression or decompression. For example, here we demonstrate a conversion using default ranges. In this type of conversion, our implementation will assume that the source array contains values within the range of uint8_t numbers and will expand it to the range of uint16_t numbers, as desired by the programmer:

>>> x = numpy.array([0,255,0,255,0,255], 'uint8').reshape(2,3)
>>> x
array([[  0, 255,   0],
       [255,   0, 255]], dtype=uint8)
>>> bob.core.convert(x, 'uint16')
array([[    0, 65535,     0],
       [65535,     0, 65535]], dtype=uint16)

The user can optionally specify source, destination ranges or both. For example:

>>> x = numpy.array([0, 10, 20, 30, 40], 'uint8')
>>> numpy.allclose(bob.core.convert(x, 'float64', source_range=(0,40), dest_range=(0.,1.)),numpy. array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ]))
True

Any range not specified is assumed to default on the type range.

Random Number Generation¶

You can build a new random number generator (RNG) of type bob.core.random.mt19937 using one of two possible ways:

Use the default constructor, which initializes with the default seed:
```
>>> bob.core.random.mt19937()
bob.core.random.mt19937()
```
Pass a seed while initializing:
```
>>> rng = bob.core.random.mt19937(34)
```

RNGs can be compared for equality. The == operator checks if both generators are on the exact same state and would generate the same sequence of numbers when exposed to the same distributions. For example:

>>> rng1 = bob.core.random.mt19937(111)
>>> rng2 = bob.core.random.mt19937(111)
>>> rng1 == rng2
True
>>> rng3 = bob.core.random.mt19937(12)
>>> rng1 == rng3
False

The seed can be re-initialized at any point in time, which can be used to sync two RNGs:

>>> rng3.seed(111)
>>> rng1 == rng3
True

Distributions skew numbers produced by the RNG so they look like the parametrized distribution. By calling a distribution with an RNG, one effectively generates random numbers:

>>> rng = bob.core.random.mt19937()
>>> # creates an uniform distribution of integers inside [0, 10]
>>> u = bob.core.random.uniform(int, 0, 10)
>>> u(rng) 
8

At our reference guide (see below), you will find more implemented distributions you can use on your programs. To simplify the task of generating random numbers, we provide a class that mimics the behavior of boost::random::variate_generator, in Python:

>>> ugen = bob.core.random.variate_generator(rng, u)
>>> ugen() 
6

You can also pass an optional shape when you call the variate generator, in which case it generates a numpy.ndarray of the specified size:

>>> ugen((3,3)) 
array([[ 3,  1,  6],
       [ 3,  2,  6],
       [10, 10, 10]])

Logging¶

Bob provides logging capabilities to integrate log output from C++ using the python logging module. In the bob.core.log module, there exist several functions to ease up the integration and the set-up of the logging module.

In an external python module you can use the bob.core.log.setup() function to generate and initialize a logger for you:

>>> logger = bob.core.log.setup("my.module.name")

This will instantiate a logging.Logger object that you can use for logging information, such as:

>>> logger.info("This might be an interesting information...")

Now, when writing a python script, you can provide the command line option for your script, to increase the verbosity level of your script:

>>> import argparse
>>> parser = argparse.ArgumentParser()
>>> # initialize command line arguments
>>> # ...
>>> bob.core.log.add_command_line_option(parser)
>>> args = parser.parse_args([])
>>> bob.core.log.set_verbosity_level(logger, args.verbose)

Of course, you can use several loggers and set different log levels for all loggers. Anyways, the root logger logging.getLogger('bob') will always be affected by the last call.